Automated analysis of collagen networks via microscopy

Abstract

Abstract-Full understanding about the interactions between cells and their surrounding environment is needed to characterize the implication of cellular dynamics on physiology and pathology. The knowledge about the composition, the geometry and the mechanical properties of the extracellular matrix is essential for this purpose. In this manuscript, we use an established method for the characterization of 3D collagen networks at fiber resolution in confocal reflection microscopy images. Firstly, a binary mask of the entire network is obtained using steerable filtering and local Otsu thresholding. Secondly, individual collagen fibers are reconstructed by tracking maximum ridges in the Euclidean distance map of the binary mask. The approach was applied to quantify the 3D network geometry of hydrogels polymerized with different collagen concentrations in two in vitro platforms: an eight-well culture plate and a microfluidic device. Our results shown similar fiber lengths, fiber persistence lengths and cross-link densities for the fibers of the collagen hydrogels polymerized in different platforms for the same concentration, while the differences on the pore size are large reflecting on the anisotropy of the network polymerized on the microfluidic device.

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

“…To the best of authors' knowledge, this is the first study that aims at quantifying myelination using image analysis and machine learning methods. Having said that, there are studies on three-dimensional medical image segmentation and quantification [5]- [8].…”